Advancing Tools for Simulation-Based Inference

Abstract

We study the benefit of modern simulation-based inference to constrain particle interactions at the LHC. We explore ways to incorporate known physics structures into likelihood estimation, specifically morphing-aware estimation and derivative learning. Technically, we introduce a new and more efficient smearing algorithm, illustrate how uncertainties can be approximated through repulsive ensembles, and show how equivariant networks can improve likelihood estimation. After illustrating these aspects for a toy model, we target di-boson production at the LHC and find that our improvements significantly increase numerical control and stability.

26 pages, 13 figures; v2: extended results section, v3: version accepted for publication

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Cited by in corpus (5)

• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• Amplitude Uncertainties Everywhere All at Once
• BitHEP -- The Limits of Low-Precision ML in HEP
• Observable Optimization for Precision Theory: Machine Learning Energy Correlators
• Exploring the BSM parameter space with Neural Network aided Simulation-Based Inference